System and method to anonymize data transmitted to a destination computing device

ABSTRACT

A method and system for anonymizing data to be transmitted to a destination computing device is disclosed. Anonymization strategy for data anonymization is provided. Data to be transmitted is received from a user computer. Selective anonymization of the data is performed, based on the anonymization strategy, using an anonymization module. Accent preservation of data is selected. An accent value for the data is determined. The anonymized data with the determined accent value is transmitted to the destination computing device over a network. In one embodiment, the anonymized data has less number of characters than the input data.

CROSS-REFERENCE TO RELATED APPLICATIONS

NONE

TECHNICAL FIELD

The present invention relates generally to transmitting data to and retrieving data from a destination computing device and particularly, to anonymize data transmitted to the destination computing device and de-anonymize data retrieved from the destination computing device.

DESCRIPTION OF RELATED ART

The Internet has enabled computer users all over the world to interact, communicate, store and access information electronically. One particularly popular mode for running applications over the internet is to host application on a server located in a remote location, along with data associated with the applications. The users of the application access the application and associated data over the internet. Sometimes, this mode of running applications in a remote location is referred to as cloud computing. It is not uncommon for the application to be hosted on a server owned or controlled by a third party. And, various clients or users of the application run the application hosted on the server at the third party location and store data corresponding to the application on a data storage device, for example, a database running on a computing device at the third party location.

There are multiple benefits of using a third party hosted application to a client. For example, the application may be managed by subject matter experts who are familiar with the application, for example a customer relationship management (CRM) application. Sometimes, ancillary applications and resources needed to provide best in class application may be made available to all the clients of the application. The third party may be able to allocate resources based on varying or seasonal demands of each of its customers.

As more and more enterprises or customers use applications hosted at third party locations, the data associated with the enterprise may need to be protected from unauthorized access. Some of the data security requirements may be imposed by regulatory bodies. Some of the data security requirement may be client specific.

As more and more applications are hosted at the third party locations and corresponding data is stored at the third party locations, there is a need to find efficient ways to provide data security to the data stored at the third party locations. With these needs in mind, the current disclosure arises. This brief summary has been provided so that the nature of the disclosure may be understood quickly. A more complete understanding of the disclosure can be obtained by reference to the following detailed description of the various embodiments thereof in connection with the attached drawings.

SUMMARY OF THE INVENTION

In one embodiment, a method for anonymizing data to be transmitted to a destination computing device is disclosed. Data to be transmitted is received from a user computer. The data includes a plurality of characters. An accent value for the data is determined. The data is anonymized using an anonymization module, to derive an anonymized data. The anonymised data and the accent value is transmitted to the destination computer over a network. Selective anonymization of the data is performed, based on the anonymization strategy, using an anonymization module. Anonymized data is transmitted to the destination computing device over a network.

In yet another embodiment, an anonymization system to anonymize data transmitted to a destination computing device is disclosed. The system includes an anonymization strategy module to store anonymization strategy for data anonymization, a logic to receive data to be transmitted to the destination from a user computer, and an anonymization module to selectively anonymize data based on the anonymization strategy to generate an anonymized data. The received data includes a plurality of characters. An accent value for the data is determined. The anonymized data and the accent value is transmitted to the destination computing device.

In yet another embodiment, another method for anonymizing data to be transmitted to a destination computing device is disclosed. Data to be transmitted is received from a computing device. The data includes a plurality of characters. A set of characters are assigned to be part of a common character set, with characters not part of the common character set assigned to be part of an uncommon character set. The characters part of the common character set are represented by a bit stream of a first length. The characters part of the uncommon character set are represented by a bit stream of a second length, wherein the second length is longer than the first length. A bit stream for all the characters in the data is generated, based on their assignment to the common character set and uncommon character set. At least one character in the data belonging to the common character set. The generated bit stream are encoded as encoded characters to form an encoded data, using a bit stream length greater than the first length so as to generate encoded data with less number of characters than the received data.

This brief summary has been provided so that the nature of the disclosure may be understood quickly. A more complete understanding of the disclosure can be obtained by reference to the following detailed description of the preferred embodiments thereof in connection with the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features of several embodiments are now described with reference to the drawings. In the drawings, the same components have the same reference numerals. The illustrated embodiments are intended to illustrate but not limit the invention. The drawings include the following Figures:

FIG. 1A shows a block diagram of a system with anonymization system of the present disclosure used to process requests from a user system to an application server, according to one aspect of the present disclosure;

FIG. 1B shows a block diagram of the user system of FIG. 1 configured to communicate with a server over the network, according to one aspect of the present disclosure;

FIG. 2 shows a block diagram of the internal functional architecture of a user computer, according to one aspect of the present disclosure;

FIG. 2A shows a block diagram of another embodiment of a system used to service requests from a user computer, according to another aspect of the present disclosure;

FIG. 3 shows an exemplary topology of computer network coupling user computers to servers over the internet, according to one aspect of the present disclosure;

FIGS. 4 and 4A show an exemplary block diagram of an anonymization system, according to one aspect of the present disclosure;

FIG. 5 shows an exemplary anonymization strategy data table, according to one aspect of the present invention;

FIG. 6A shows an exemplary block diagram of a searchable encryption system, according to one aspect of the present disclosure;

FIG. 6B shows an exemplary table 620 showing various iterations of the encoding operation of the searchable encryption system of FIG. 6A, according to one aspect of the present disclosure.

FIG. 6C shows an exemplary table 650 showing various iterations of the decoding operation of the searchable encryption system of FIG. 6A, according to one aspect of the present disclosure.

FIGS. 7A-7C show exemplary data fields for anonymizing input word with accents, according to one aspect of the present disclosure;

FIG. 7D show exemplary data fields for anonymizing input word with case sensitive information, according to one aspect of the present disclosure;

FIGS. 7E-7F show exemplary data fields for anonymizing input word with compression, according to one aspect of the present disclosure;

FIGS. 8A-8D shows exemplary anonymization of data transmitted to a destination computing device;

FIGS. 9A-9C shows exemplary de-anomymization of data received from the destination computing device;

FIG. 10 shows method of anonymizing data to be transmitted to a destination computing, according to one aspect of the present disclosure;

FIG. 11A shows method of processing a request for stored anonymized data by the destination computing device; and

FIG. 11B shows method of processing selectively retrieved anonymized data from the destination computing device.

DETAILED DESCRIPTION

To facilitate an understanding of the adaptive aspects of the present invention, the general architecture and operation of a networked system is described. The specific architecture and operation of the adaptive aspects of the present disclosure are then described with reference to the general architecture.

FIG. 1A shows a top-level block diagram of a system 100 with anonymization system of this disclosure that is used to send data from a user system, according to one aspect of the present disclosure. System 100 includes a user system 102 that is coupled via a gatway 104 and a network connection 106 to a server 110 through another gateway 108. In one aspect, server 110 is a web-server. Gateway 104 in one embodiment includes an anonymization system 112 of this disclosure. Database 114 is used to store information that is accessed by the user system 102. In one aspect, database 114 is a structured query language (SQL) based database.

FIG. 1B is a block diagram of a user system 102 according to one embodiment of the present disclosure, which is configured to communicate with the server 110 over the network. FIG. 1B includes a user computer 116 (sometimes referred to as computer 116) and a monitor 118. Monitor 118 may be a CRT type, a LCD type, a plasma type, or any other type of color or monochrome display. Also provided with computer 116 is a keyboard 120 for entering text data and user commands, and a pointing device 122 (such as a mouse) for processing objects displayed on monitor 118. In some embodiments, objects displayed on monitor 118 may be elements of a graphical user interface.

Computer 116 may include a computer-readable memory medium such as a rotating disk 124 for storing readable data. Besides other programs, disk 124 can store application programs including web browsers by which computer 116 connects to a network and the systems described below, according to one aspect of the present disclosure. In some embodiments, disk 124 may be a disk system with a plurality of disk drives arranged as a Redundant Array of Inexpensive Drives (RAID) system, accessible using a storage adapter (not shown).

Computer 116 can also access a computer-readable storage device 126 with removable storage media storing data files, application program files, and computer executable process steps embodying the present invention or the like. For example, the storage device 126 may be a CD-ROM or a DVD ROM. In some embodiments, the storage device 126 may support removable storage media that is read-only device (R), write once-read many (WORM), or rewriteable (RW) type. In some embodiments, the storage device 126 may also be provided with computer 116 to access application program files, audio files and data files stored on a removable storage media. In some embodiments, the removable storage media may be optical, magnetic, magneto-optic, or semiconductor based recording media.

A modem, an integrated services digital network (ISDN) connection, wireless or the like also provides computer 116 with a DSL/Cable/satellite/wireless (or Internet connection) 128 to the World Wide Web (WWW). Internet connection 128 allows computer 116 to send and receive commands, data files, audio files, application program files and computer-executable process steps embodying the present invention.

Computer 116 is also provided with external audio speakers 130A and 130B to assist a listener to listen to music either downloaded on-line from the Internet or off-line using a storage medium. It is noteworthy that a listener may use headphones instead of audio speakers 130A and 130B to listen to music.

FIG. 2 is a block diagram showing the internal functional architecture of computer 116. As shown in FIG. 2, computer 116 includes a central processing unit (CPU) 201 for executing computer-executable process steps and interfaces with a computer bus 208. Also shown in FIG. 2 are a WWW interface 202, a display device interface 203, a keyboard interface 204, a pointing device interface 205, an audio interface 209, video interface 210, printer interface 212, and a disk 124. Audio Interface 209 allows a listener to listen to music, Online (downloaded using the Internet or a private network) or offline (using a CD).

As described above, disk 124 may store operating system program files, application program files, web browsers, and other files. Some of these files are stored on disk 124 using an installation program. For example, CPU 201 executes computer-executable process steps of an installation program so that CPU 201 can properly execute the application program.

A random access main memory (“RAM”) 206 also interfaces to computer bus 208 to provide CPU 201 with access to memory storage. When executing stored computer-executable process steps from disk 124 (or other storage device such as storage device 126 or Internet connection 128), CPU 201 stores and executes the process steps out of RAM 206.

Read only memory (“ROM”) 207 is provided to store invariant instruction sequences such as start-up instruction sequences or basic input/output operating system (BIOS) sequences for operation of keyboard 120.

FIG. 2A shows yet another example of the overall system 100, according to one aspect of the present disclosure. In FIG. 2A, server 110 is shown as a web server and database 114 is shown as a SQL (structured query language) database. FIG. 2A also shows the various steps that are used to access database 114. In step 1, a user using a browser running on the user system 102, submits a URL, as a request. URL includes the application to which the request is directed. In step 2, the web server 110 captures the request, and locates and executes corresponding application specific program (ASP) code for the application to which the request was directed.

In step 3, during execution of ASP code, a structured query language (SQL) code is generated and executed. Based on the executed SQL code, database 114 is accessed to fetch, add, modify or delete information in the database.

In step 4, the results are sent to the web server 110. The web server 110 constructs a response, for example as a HTML code. In step 5, HTML code is sent to the user system 102. In step 6, the HTML page is sent to the browser and displayed on the user system 102.

Although in this example, a user submits a URL as a request, in some embodiments, a user application executing on the user computer 102 may submit a request. The request may be submitted as a URL, and the user application may be configured to receive the response to the request.

Now, an exemplary coupling of the computer 116 to a server over the internet will be described with reference to FIG. 3. FIG. 3 shows an exemplary topology of a computer network with computers similar to computer 116, connected to the Internet 302. For illustration purposes, three computers X, Y and Z are shown connected to the Internet 302 via Web interface 202 through a gateway 301, where gateway 301 can interface N number of computers. Gateway 301 may be similar to gateway 104 with an anonymization system 112. Web interface 202 may be a modem, network interface card or a unit for providing connectivity to other computer systems over a network using protocols such as X.25, Ethernet or TCP/IP, or any device that allows, directly or indirectly, computer-to-computer communications. Gateway 301 and computers X, Y and Z may be located or controlled within a user controlled environment. This user controlled environment may be within the user controlled enterprise or intranet. For convenience, gateway 301 and computers X, Y and Z are grouped together and referred to as user cloud 309. Exemplary topology of a computer network may have additional groups of computers with gateway to define additional user clouds. In one embodiment, data flow outside the user cloud may need special handling.

It is noteworthy that the invention is not limited to a particular number of computers. Any number of computers can be connected to the Internet 302 or any other computer network may be used.

FIG. 3 further shows a second gateway 303 that connects a network of web servers 304 and 305 to the Internet 302. Web servers 304 and 305 may be connected with each other over a computer network. Web servers 304 and 305 receive requests from the user computer and respond to the requests received from the user computer. Web server 304 is coupled to a database 306 and web server 305 is coupled to a database 307. In one embodiment, the web server may be hosting an application for use by the user computer. As an example, web server 304 is hosting server application SAO and web server 305 is hosting server application SA1. As one skilled in the art appreciates, server applications may be a hosted customer relationship management software (CRM) application, a website, online shop, news service, search applications, social networking applications, blog sites, webmail and the like.

In one embodiment, the gateway 303, servers 304 and 305, and databases 306 and 307 may be hosted at a third party location. For convenience, gateway 303, servers 304 and 305, and databases 306 and 307 are grouped together and referred to as hosted cloud 310. Exemplary topology of a computer network may have additional groups of servers with gateways and databases to define additional hosted clouds.

The following provides a brief description of the Internet. The Internet connects thousands of computers world wide through well-known protocols, for example, Transmission Control Protocol (TCP)/Internet Protocol (IP), into a vast network. Information on the Internet is stored world wide as computer files, mostly written in the Hypertext Mark Up Language (“HTML”). Other mark up languages, e.g., Extensible Markup Language (“XML”) as published by W3C Consortium, Version 1, Second Edition, October 2000, ©W3C may also be used. The collection of all such publicly available computer files is known as the World Wide Web (WWW). The WWW is a multimedia-enabled hypertext system used for navigating the Internet and is made up of hundreds of thousands of web pages with images and text and video files, which can be displayed on a computer monitor. Each web page can have connections to other pages, which may be located on any computer connected to the Internet.

A typical Internet user uses a client program called a “Web Browser” to connect to the Internet. A web browser is a software program that allows users to access the content stored in Web sites. Modern Web browsers can also create content “on the fly”, according to instructions received from a Web site. This concept is commonly referred to as “dynamic page generation”. In addition, browsers can commonly send information back to the Web site, thus enabling two-way communication of the user and the Web site. A user can connect to the Internet via a proprietary network, such as America Online, or via an Internet Service Provider, e.g., Earthlink. The web browser may run on any computer connected to the Internet. Currently, various browsers are available of which two prominent browsers are Microsoft Internet Explorer and Mozilla Firefox. The Web Browser receives and sends requests to a web server and acquires information from the WWW. A web server is a program that, upon receipt of a request, sends the requested data to the requesting user. A standard naming convention known as Uniform Resource Locator (“URL”) has been adopted to represent hypermedia links and links to network services. Most files or services can be represented with a URL.

URLs enable Web Browsers to go directly to any file held on any WWW server. Information from the WWW is accessed using well-known protocols, including the Hypertext Transport Protocol (“HTTP”), the Wide Area Information Service (“WAIS”) and the File Transport Protocol (“FTP”), over TCP/IP protocol. The transfer format for standard WWW pages is Hypertext Transfer Protocol (HTTP).

Web domain is an Internet address that provides connection to a Web server.

Generally, URLs have three parts: the first part describes the protocol used to access the content pointed to by the URL, the second contains the directory in which the content is located, and the third contains the file that stores the content: <protocol>:<domain><directory><file>.

For example:

http://www.ciphercloud.com/bios.html

http://www.ciphercloud.com/index.html

http://fn.cnn.com/archives/may2009/pr3.html

ftp://user1.ciphercloud.com/software/pages.zip

Commonly, the <protocol> part may be missing. In that case, modern Web browsers access the URL as if the http://prefix was used. In addition, the <file> part may be missing. In that case, one of the convention calls for the file “index.html” to be fetched. In some cases, the request may be redirected to fetch another default file.

For example, the following are legal variations of the previous example URLs:

www.ciphercloud.com/bios.html

www.ciphercloud.com

fn.cnn.com/archives/may2009/pr3.html

ftp://userl.ciphercloud.com/software/pages.zip Web Page.

Web page is the content associated with a URL. In its simplest form, this content is static text, which is stored into a text file indicated by the URL. However, very often, the content contains multi-media elements (e.g. images, audio, video, etc) as well as non-static text or other elements (e.g. news tickers, frames, scripts, streaming graphics, etc). Very often, more than one file forms a Web page. However, there is only one file that is associated with the URL and which initiates or guides the Web page generation.

When a Web browser receives an URL, the Web browser requests a Domain Name System (DNS) name server to decode the domain name and return the IP address for the domain name. The DNS name server returns the IP address of the domain name as stored in the DNS name server to the web browser. Web browser uses the IP address for the domain name and sends a request corresponding to the requested URL that confirms to the HTTP protocol to the IP address. In one embodiment, the request is sent using TCP/IP protocol over the internet.

In one embodiment, the user computer 116 sends requests to the server using HTTP protocol. As previously described with reference to FIG. 2A, the request is processed by the web server and a response is sent to the user computer. In the exemplary topology described with reference to FIG. 3, the request is sent over the internet to the server. In some embodiment, requests may be sent using Hypertext Transfer Protocol Secure (HTTPS) protocol, which is a combination of HTTP protocol with SSL/TLS protocol to provide encrypted communication and secure identification of a network web server.

Now, one embodiment of an anonymization system 402 is described with reference to FIG. 4. Anonymization system 402 may be similar to anonymization system 112. FIG. 4 shows the anonymization system 402 as part of gateway 400 that connects to the network, but the anonymization system 402 can be provided in other ways, such as software running on a server, distributed software, or various software and hardware packages operating together. In one embodiment, the anonymization system 402 may be part of the gateway described with reference to FIG. 1A and FIG. 3. The gateway 400 is configured to communicate with the user systems within the user cloud and communicate with the hosted cloud over the network. The anonymization system 402 will be described in detail, with reference to FIG. 4A.

Now referring to FIG. 4A, the anonymization system 402 includes a management console module 404, an anonymization strategy module 406, anonymization module 408, de-anonymization module 410, tokenization module 412 and one or more crypto modules, shown as crypto 1 module 416, crypto 2 module 418 and crypto N module 420. Additionally, a token vault 414 is also part of the anonymization system 402. As one skilled in the art appreciates, various modules referenced in this disclosure may be implemented in hardware, software executed on a computing device or a combination of hardware and software. Additionally, various modules may be implemented as an integrated unit or may be implemented as separate functional units that interact with each other using a communication protocol.

The management console module 404 is configured to communicate with various applications running on the hosted cloud, over the network. Additionally, the management console module 404 is also configured to communicate with the user computer. For example, the management console module 404 may send queries to various applications running on the hosted cloud and receive meta data (or data schema, data definitions) of each application.

The management console module 404 may also present the received meta data information about the applications running on the hosted cloud to the user. The management console module 404 may additionally present the available anonymization strategies for each data field of the meta data to the user computer. The user may select one or more of the anonymization strategies to be applied to various data fields of the application, using the user computer.

The selected anonymization strategy is stored by the management console module 404 in the anonymization strategy module 406, for later use by other modules of the anonymization system 402. The anonymization strategy may be stored as a table in the anonymization strategy module 406. The anaonymization strategy in one embodiment may include schema definition for data manipulation. An exemplary selected anonymization strategy will be described later with reference to FIG. 5.

The anonymization module 408 is configured to intercept any data to be transmitted from a user computer to the hosted cloud. The anonymization module 408 is also configured to communicate with the anonymization strategy module 406 and evaluate various fields of data to be transmitted against anonymization strategy stored in the anonymization strategy module 406. Based upon this evaluation, the anonymization module 408 is configured to perform anonymization of one or more data fields using one or more of the tokenization module 412 and crypto modules, for example, cryptol module 416 and crypto 2 module 418 and generate corresponding anonymized data field.

The anonymization module 408 is also configured to reassemble the data to be transmitted to the hosted cloud, using the anonymized data fields. In one embodiment, the reassembled data includes one or more unique data pattern added to the anonymized data fields to indicate the selected anonymization strategy used to anonymize the data fields. In one embodiment, a preamble is appended to the beginning of the anonymized data field. In one embodiment, a postamble is appended to the end of the anonymized data field. The reassembled data is forwarded to the hosted cloud over the network using the gateway 400. Various functions and features of the tokenization module 412 and crypto modules will be described later.

The de-anonymization module 410 is configured to intercept any data received by the user computer from the hosted cloud. The de-anonymization module 408 is also configured to evaluate various fields of data received from the hosted cloud to detect and decode the preamble and postamble of the data fields. Based upon this evaluation, the de-anonymization module 410 is configured to perform de-anonymization of one or more data fields using one or more of the tokenization module 412 and crypto modules, for example, cryptol module 416 and crypto 2 module 418 and generate corresponding de-anonymized data field. The de-anonymization module 410 is also configured to reassemble the data to be transmitted to the user computer, using the de-anonymized data fields. The reassembled data is forwarded to the user computer.

Now, referring to FIG. 5, an exemplary anonymization strategy data table 500 for server application SAO stored by the anonymization strategy module 406 is described. As one skilled in the art appreciates, the anonymization strategy data may be stored in other forms other than a table form. Column 502 shows various data fields of server application SA0. Each of the rows of table 500 shows the data field and their corresponding characteristics. As an example, Column 504 shows data type for each of the data fields, for example numeric, alphabetical or alpha-numeric characters. Column 506 shows the length of the data field. Column 508 shows whether any portion of the data field needs to be retained as originally provided by the user computer. Column 510 shows the selected anonymization strategy for each of the data fields.

Now referring to row 512, various attributes of DATA FIELD 1 is stored in table 500. For example, DATA FIELD 1 is a numeric field of length 10 characters. Characters 8 through 10 of DATA FIELD 1 needs to be retained in its original form. The selected anonymization strategy for DATA FIELD 1 is TPF 1. For example, DATA FIELD 1 may be a telephone number and characters 10:08 may represent the area code of the telephone number and may have to be maintained in its original form at the hosted cloud. However, characters 07:01 will be anonymized using anonymization strategy TPF1. In one embodiment, TPF1 may correspond to anonymization using tokenization module 412. In one embodiment, TPF1 may correspond to anonymization using Crypo 1 module 416. In one embodiment, a preamble indicative of TPF1 anonymization strategy may be appended to the anonymized DATA FIELD 1.

Now referring to row 514, various attributes of DATA FIELD 4 is stored in table 500. For example, DATA FIELD 4 is an alphabetic field of length 8 characters. No characters need to be retained in its original form. The selected anonymization strategy for DATA FIELD 4 is TRF4. In one embodiment, TRF4 may correspond to anonymization using crypto 2 module 418. In one embodiment, TRF4 may correspond to anonymization using crypto N module 420. In one embodiment, a preamble indicative of TRF4 anonymization strategy may be appended to the anonymized DATA FIELD 4.

Now referring to row 516, various attributes of DATA FIELD 8 is stored in table 500. For example, DATA FIELD 8 is an alpha-numeric field of length 24 characters. No characters need to be retained in its original form. The selected anonymization strategy for DATA FIELD 8 is none. Based on this selection, no anonymization of DATA FIELD 8 will be performed and the data contained in DATA FIELD 8 will be sent in its original form.

Now various anonymization techniques implemented in various anonymization modules like tokenization module 412 and crypto modules like crypto 1 module, crypto 2 module and the like will be described. The anonymization technique selected for a data field may be based upon multiple factors. One of the factors is level of desired security. One of the other factors is data attribute preservation for the data field. The data attribute preservation refers to retaining capabilities to perform operations on the stored data field in their anonymized form as compared to the stored data in its original or clear form. Some of the operations that may be performed on the stored data field in their anonymized form include sorting in the order of their clear data form (or original data form), searching, searching regardless of the case of the characters, searching using partial string match or wild card match. As one skilled in the art appreciates, as more data attributes are preserved in the stored data form, in some embodiments, it may lead to decreased level of data security. Clear data or original data refers to the data as present in the data field before anonymization.

Anonymization techniques may be broadly divided into two categories. One, a token based anonymization. The token based anonymization may be implemented in the tokenization module 412 and may require local storage of the tokens in the token vault 414. Another technique is to use a symmetric key encryption based anonymization. Symmetric key encryption based anonymization technique may be implemented in one or more of the crypto modules, for example, crypto module 1, crypto module 2 and the like. Now, various anonymization techniques for each of the token based anonymization and symmetric key encryption based techniques will be described.

Token based anonymization techniques:

Random token anonymization: In this technique, for data contained in each data field, a corresponding token is created. The token vault 414 is used to store the relationship between the original or clear data and the corresponding token. The token is sent to the hosted cloud, after prepending a preamble indicative of the type of anonymization performed on the data field. When the data field is retrieved from the hosted cloud, based on the preamble indicative of the type of anonymization performed, the token is used to extract the corresponding clear data stored in the token vault 414. The de-anonymized data is then forwarded to the user computer.

Sortable token anonymization: In this technique, the token vault is organized in a balanced, ordered tree, with each node representing a specific token that corresponds to a specific clear data. For an input data, the tokenization module 412 looks up the tree in the token vault 414 for a node that has not been used for clear data corresponding to the input data and establish a new token that corresponds to the input data. The new token is mapped to the input data. The new token in the selected node maintains the order of the tree. In this way, the token continues to maintain the order that corresponds to the original or clear data. The order may correspond to the position, sequence, or value of the original data. For example, the order may be based on alphabetical sequence, numerical sequence, value of the characters based on an assigned value or value of the characters based on a pre-assigned value, for example, Unicode value. The new token is then sent to the hosted cloud.

Token anonymization that allows case-insensitive search: In one embodiment, for each data, two tokens are generated. One, based on the original or clear form of the data and the other based on all lower cases. In one embodiment, random token anonymization technique is used, as described above. Both of the generated tokens are sent to the hosted cloud, with the token corresponding to all lower case is stored in a hidden field in the application.

When a search request is received for this field from the user computer, for example, as a keyword in clear form, search request is generated by the anonymization system 402, for both the clear form and all lower case. For example, anonymized keywords are generated, one based on the keyword in its original form and another based on keyword with all lower cases. The search request is then sent to the host cloud, with anonymized keyword for both clear form and all lower case. In one embodiment, the search request is sent with an “OR” logic so as to receive search results for both the keywords. Any results of the search received from the host cloud for both the anonymized keyword for clear form and anonymized keyword for all lower case will then be de-anonymized by the de-anomymization module of the anonymization system. The de-anonymized results are then presented to the user computer. As one skilled in the art appreciates, a keyword received in a search request may be full data field, few characters of a data field or few characters with a wild card. Principles described herein will work for all of these cases, as the search request sent to the host cloud will be based on anonymized keyword.

Searchable encryption: In this technique, encryption algorithm is designed to encrypt words of text. The encrypted values preserve the properties of sorting and equality of the clear values. As an example, the equality can be either case-sensitive or case-insensitive. As another example, sorting may be either case-sensitive or case-insensitive. In one embodiment, accented characters may be treated as equal characters. i.e. a character “E” and “E” may be treated as equal or same. In one embodiment, accented characters may be treated as adjacent characters, with “E” positioned after “E”. Some of these techniques may permit both order preserving property for the anonymized data and case-insensitive search capability. Exemplary searchable encryption to encode words of a text will now be described with reference to FIGS. 6A-6B.

FIG. 6A shows an exemplary block diagram of a searchable encryption system 600. The searchable encryption system 600 may be implemented in the anonymization system 402. The searchable encryption system 600 includes an input logic 602 to receive input word to be encrypted, an encryption engine 604, a compare logic 606 and an encode logic 608. Various elements of the encryption system 600 may be implemented in various functional modules of the anonymization system 402, for example, in the anonymization module 408, crypto modules 416-418, de-anonymization module 410 and the like. For example, the encryption engine 604 may be part of one of the crypto modules 416-420.

The input logic 602 is configured to receive the input word to be fed to the encryption engine 604. In one embodiment, the encryption engine 604 receives each of the characters of the input word and generate a random character as an output. The encryption engine 604 may include a Pseudo-Random Number Generation Algorithm (PRNG) to generate a random character as an output. The PRNG is preferably secure and is fast. One exemplary PRNG may be a RC4 cipher. In one embodiment, the generated random character will be within a predefined range of characters, which narrows in subsequent iterations, as will be described later.

The output of the encryption engine 604 is fed to the compare logic 606. The compare logic 606 is also configured to receive the corresponding input character as an input. The compare logic 606 is configured to compare generated output character of the encryption engine 604 with the corresponding input character in a first range of characters and generate a binary bit, based on whether the input character is higher in position or lower in position relative to the generated output character. As an example, a bit 0 is generated if the position is lower and a bit 1 is generated, if the position is higher relative to the generated output character. The generated output character is now fed as input to the encryption engine 604 to generate a second generated output character, within a second range of characters.

The second generated output character is again compared with the input character by the compare logic 606 and generate next bit of output based on the position of the input character relative to the second generated output character, within the second range of characters (which is smaller than the first range). The operation is repeated to generate additional output bits until the range is such that there are only two characters before or after the input character. Now, the next character of the input word is processed to generate second set of output bits. This operation will be further described with reference to FIG. 6B with table 620.

Referring to FIG. 6B, table 620 shows for each iteration in column 622, the corresponding character range in column 624 and the output of the compare logic for each iteration in column 626. For simplicity, only words with uppercase characters will be described. For example, if the word to be encoded is “GOLD”, initial encoding starts with encoding the first character “G”. The character G is fed to the encryption logic 604 as a first input character and the encryption logic 604 is configured to generate a random character as an output, for example, a random character between A-Z, which defines the first range of characters. Let us assume, the first generated random character output is Q.

Now, the first input character “G” is compared with the first generated random character output “Q” by the compare logic 606 to see if it is higher in position or lower in position relative to the first generated random character output, within the range shown in cell 628 which corresponds to the first iteration 630. In this case, as character G is lower in position to the first generated random character output Q, a binary value of “0” is output as the first bit of the encoded data, as shown in cell 632. Additionally, the first generated random character output Q defines the upper bound (as character Q is higher in position relative to character G) for the range of characters to be used in the next iteration, for example, second range of characters A-Q.

Next, the first generated random character output (in this case character “Q”) is input to the encryption logic 604 and the encryption logic outputs a second generated random character output, within the second range of characters, as shown in cell 634. As one skilled in the art appreciates, the second range of characters is A-Q and the range of characters the encryption logic can output will be configured to be between A-Q. Let us assume, the second generated random character is character “C”.

Now, the first input character is again compared with the second generated random character output to see if it is higher in position or lower in position relative to the second generated random character output. In this case, as character G is higher in position to the second generated random character output C, a binary value of “1” is output as the second bit of the encoded data, as shown in cell 636. Additionally, the second generated random character output C defines the lower bound (as character C is lower in position relative to character G) for the range of characters to be used in the next iteration, for example, third range of characters C-Q.

Now, the second generated random character output C is input to the encryption engine 604 and the encryption engine 604 outputs a third generated random character output. In this case, the third range of characters the encryption engine 604 can output is be configured to be within the third range of characters C-Q. Let us assume, the third generated random character is character “E”, with a “1” assigned to the third bit of the encoded data, based on the comparison. Additionally, the third generated random character output E defines the lower bound (as character E is lower in position relative to character G) for the range of characters to be used in the next iteration, for example, fourth range of characters E-Q.

Similarly, referring to iteration 4, fourth generated random character is character “J” with an output of “0” as the forth bit of the encoded data, based on the comparison. Additionally, the fourth generated random character output J defines the upper bound (as character J is higher in position relative to character G) for the range of characters to be used in the next iteration, for example, fifth range of characters E-J.

Similarly, referring to iteration 5, the fifth generated random character is character “H”, with a “0” assigned to the fifth bit of the encoded data, based on the comparison. This process is repeated until there are two or less characters left before and after the first input character, at which time, the encoding stops. So, in this case, as there are only two characters E and F before and one character J after the first input character G, the encoding stops. So, the final encoded output for character “G” is “00110”.

The rest of the characters “O”, “L” and “D” are similarly processed to get their corresponding encoded output. As an example, let the final encoded output for “GOLD” is 010010001010010100000110. As one skilled in the art appreciates, depending upon the random character generated by the encryption engine 604, each character may have different number of bits as their encoded output. Now the final encoded output is transformed into a character representation, using an encode logic 608, which will be described now.

In one embodiment, the character representation of the final encoded output is done by splitting the final encoded output into a plurality of bits of data and assigning a unique character to each unique combination of a plurality of bits. As an example, four bits may be used to represent 16 different unique combinations, with each combination assigned a specific character. For example, a base-16 encoding may be used and sixteen characters, characters B-Q assigned to the base-16 encoded data. As an example, 0000 may be encoded as character B and 1111 may be encoded as character Q. With base-16 encoding, other characters may be available for assignment for special purposes or for padding. Other random assignments of characters to the base-16 encoded data are also possible. In some embodiments, dummy characters may be padded in the beginning and the end of the range, so that a lowest character in the character range will not encode as all zeros and highest character in the character range will not encode as all ones.

In one embodiment, in order to provide case insensitive search capability, the encoded output is mapped into lower-case characters, for example, characters b-q. Then, specific characters of the mapped word is capitalized, based on the capitalization of the original input word. For example, if the input word was Mom, and the final output is mapped into character combination “def”, then, based on the capitalization of the original input word, the mapped character combination is modified to be “Def”. A case sensitive and a case insensitive search on “mom” would return the appropriate results.

In some situations, the final encoded output may result in a mapped word that may have less characters than the original input word. As an example, for a three character input word, the encoded output may only generate two mapped characters. In such a situation, at least one character that is not used in the output mapping (as only 16 characters are used in a base-16 encoding) may be used as a padding character. Preferably, the padding character should sort before all other used characters. As an example, if the characters B-Q are used for mapping, character A may be used as a padding character to be added to the mapped characters. Character A would sort before the characters B-Q.

As an example, if the input of Mom mapped into “De”, then with the added padding, the modified mapped characters will be Dea, which will have the same number of characters as the original input character. If the original input was “moM” and the input mapped into “De”, then the modified mapped characters will be “deA”, which preserves both the number of characters of the original input and the case of the characters of the original input.

In the above examples, only characters A-Z were considered. In order to expand the search and sortable encryption capabilities to characters outside the A-Z range, one or more escape characters may be pre-pended and/or post-pended to the input word. The escape character may indicate the characteristics of the word being encrypted. As an example, unique escape characters may be pre-pended to the input word to indicate if the input word is a character less than the number “0”, numbers between 0-9, numbers greater than 9 but less than A, lettes A-Z, single byte character greater than “Z”, Unicode character in the range U+0080 to U+00FF, Unicode character in the range U+0100 to U+FFFF, Unicode character in the range U+10000 to U+10FFFF and the like. Additionally, a unique escape character may be post-pended to the input word to indicate “end of word”.

As one skilled in the art appreciates, in one embodiment, the searchable encryption system 600 may additionally have a decode logic 612 to receive the encoded word 614 and output the decoded word character string. The decode logic 612 would receive the encoded characters of the encoded word as an input, perform the manipulation necessary to decode the encoded characters one character at a time and reassemble the encoded word as a decoded word. An exemplary decoding operation will be further described with reference to FIG. 6C with table 650.

Referring to FIG. 6C, table 650 shows for each iteration in column 652, the input bit 654 from the encoded word, corresponding character range in column 656. For simplicity, only decoding of character “G” of the encoded word will be described. For example, the character “G” was encoded as “00110”. Iteration 0 refers to the initial set up of the range of characters, for example, first range of characters 658, which will be from A-Z. In iteration 1, the input bit is “0” and the input bit “0” is fed to the decode logic 612. The decode logic 612 generates first random range character Q as an output, based on the input bit of “0”. Since input bit was a “0”, character Q defines the upper bound for the next range of characters to be used in the decoding process. Now, the second range of characters 660 is defined to be from A-Q, based on the generated first random range character “Q”.

Next, in iteration 2, next input bit of “1” is input to the decode logic 612. The decode logic 612 generates second random range character C as an output, based on the input bit of “1”. As the input bit was a “1”, character C+1 defines the lower bound for the next range of characters to be used in the decoding process. Now, the third range of characters 662 is defined to be from D-Q, based on the generated second random range character “C”.

Next, in iteration 3, next input bit of “1” is input to the decode logic 612. The decode logic 612 generates third random range character E as an output, based on the input bit of “1”. As the input bit was a “1”, character E+1 defines the lower bound for the next range of characters to be used in the decoding process. Now, the fourth range of characters 664 is defined to be from F-Q, based on the generated third random range character “E”.

Next, in iteration 4, next input bit of “0” is input to the decode logic 612. The decode logic 612 generates fourth random range character J as an output, based on the input bit of “0”. As the input bit was a “0”, character “J” defines the upper bound for the next range of characters to be used in the decoding process. Now, the fifth range of characters 666 is defined to be from F-J, based on the generated fourth random range character “J”.

Next, in iteration 5, next input bit of “0” is input to the decode logic 612. The decode logic 612 generates fifth random range character H as an output, based on the input bit of “0”. As the input bit was a “0”, character “H” defines the upper bound for the next range of characters to be used in the decoding process. Now, the sixth range of characters 668 is defined to be from F-H, based on the generated fifth random range character “H”.

Now, the fifth range of characters 668 is narrowed down to three characters, F, G and H. The middle character “G” is chosen as the decoded character for the encoded input word. Similarly, rest of the encoded input words are decoded to generate the original word “GOLD”.

Accented Characters

In one embodiment, encryption of accented characters are performed. The search capabilities and sort capabilities are retained during the encryption of the accented characters. As an example, let the clear form of the input word with accents be “éleve”. In this example, the first character e has an accent and rest of the characters do not have an accent. In this example, let us assume that accent preserving capability is required for characters in Unicode space U+0000 to U+0180. As one skilled in the art appreciates, this range of Unicode space covers most European languages. The first character “e” has 10 possible accents. The second character “1” has 6 possible accents. The fourth character “v” has no accents. So, for the input word “eleve” there are a total of 10×6×10×1×10=6000 possible variations of accents. So, maximum accent value for the input word “eleve” is 6000.

In one embodiment, the letter “é” may be assigned a value 2 (for example, based on its position 2 in the list of 10 variations, namely,

the letter “l” may be assigned a value 0 (for example, based on its position 0 in the list of 6 variations, namely,

, the letter v assigned a value of 0 (for example, based on its position 0 in the list of 1 variation, namely “v”). So, based on these assigned value, the input word “éleve” will have a value of ((((2*6)+0)*10+0)*10+0)*1+0=1200, out of the 6000 possible different ways the word “eleve” could be accented. As one skilled in the art appreciates, the value of second “e” and the third “e” in the word “éleve” have an assigned value of 0, based on their position 0 in the list of 10 variations, namely,

. So, the accent value for the input word “éleve” in this case will be 1200. As one skilled in the art appreciates, other values may be assigned for each of the characters in the input word, based on their accent and a unique accent value may be derived for the input word.

The input word is stripped of all accents and encrypted. For example, let the input word without any accent, “eleve” encrypts into encrypted word “defghi”. In one embodiment, the accent value of the input word is appended to the encrypted word. As an example referring to FIG. 7A, an exemplary combination of the encrypted word 702 with accent value 704 is shown. In this example, the encrypted word 702 is “defghi” and the accent value is 1200, for an input word “éleve”.

In one embodiment, a set of characters representing the accent value may be appended to the encrypted word. As an example, in one embodiment, one of the characters in the character set may be used to generate the accent value. For example, a character having multiple accent combination may be used to generate the accent value.

As an example, character “e” may be used to generate the accent value. As one skilled in the art appreciates, using the example assignment of values for various accents for character “e” as described above, a character combination of “èéee” (which may be referred to as “accent value word”) provides a value of 1200, the value of the accented input word. So, the accent value word “èéee” is appended to the encrypted word “defghi” to indicate the accent value of the input word. In one embodiment, the encrypted word is separated from the accent value word by an end of word indicator. The end of word indicator may be one or more characters of a predefined special characters. As an example, character “a” may be used as an end of word indication. In this specific example, the final encrypted word combination with accent indicator will be “encrypted word+end of word indicator+accent value word”. More specifically, in this example, the final encrypted word will be “defghiaèéee”.

As an example referring to FIG. 7B, an exemplary combination of the encrypted word 702 with accent word 706 and an end of word indicator 708 is shown. In this example, the encrypted word 702 is “defghi” and the accent word is “èéee”, for an input word “éleve”. The end of word indicator 710 is letter “a”.

As one skilled in the art appreciates, the final encrypted word combination retains a search and sort capability of the input word, so long as the encrypted word (here “defgh”) retains the search and sort capability. This is because all the final encrypted word combination will include the end of word indicator and the accent value word, which in this specific example, will be character “a” and a combination of characters “e”, in different accents, based on the accent value. So, to search “eleve”, the search string “defghiaeeee” will be sent, for example, as part of a query to the database. The database will receive the query and look for matches. In some embodiments, the database may be set up to match both accented and non-accented forms. The search string “defghiaeeee” will match with string “defghiaèéee”, which is the encrypted form of “éleve”, as the database will return both accented and non-accented forms.

In some embodiments, the database may be configured to position accented forms and non-accented forms in sequence. Therefore, the non-accented form “defghiaeeee” and accented form “defghiaèéee” may be positioned in sequence. As an example, “defghiaeeee” will sort next to “defghiaèéee” in the database. In response to a sort request, “defghiaeeee” will sort next to “defghiaèéee”. So, upon decoding, “eleve” and “éleve” will appear next to each other in the output presented to a user, as a response to a sort request.

As one skilled in the art appreciates, in the above example, for an input word “éleve” with five characters, the final encrypted word combination “defghiaèéee” has eleven characters. The additional characters in this case, which is six characters, are overhead characters in the encryption process. For example, four additional characters are added to indicate the accent value for the input word. In some embodiments, it may be desirable to have less overhead characters. One exemplary example, with less overhead characters will now be described.

In one embodiment, the encrypted word may be used to indicate the accent value of the input word. As previously described, the input character “éleve” when encrypted without the accent results in an encrypted word “defghi”. And, the accent value of the input word is 1200 (from a possible 6000 values). Now, each character of the encrypted word itself may have different accent combinations. For example, the character “d” has three variations, character e has 10 variations, character f has no variations (or one value) character g has 5 variations, character h has 3 variations and character i has 9 variations. So, the possible combination of accent values to the encrypted word “defghi” is 3×10×1×5×3×9=4050. So, the maximum accent value for the encrypted word “defghi” is 4050.

In one embodiment, the encrypted word “defghi” may be modified to have accents that represent the accent value of the input word, in this case, a value of 1200. As one skilled in the art appreciates, if the accent value of the input word is greater than 4050, there will be a need to add additional characters to indicate a higher value. For example, by just adding an additional character “e” to the encrypted word, which provides 10 variations, the available unique accent value will be 4050×10=40500. The maximum accent value increases from 4050 to 40,500, with the addition of additional character “e” to the encrypted word. The available unique accent value now is more than enough to represent 6000 unique values of the input word.

As one skilled in the art appreciates, according to an embodiment of this disclosure, the encrypted word may be advantageously accented to represent the accent value of the input word. This minimizes the total number of characters of the final encrypted word. In one embodiment, if the possible accent values of the input word is greater than the possible accent values of the encrypted word, one or more additional characters may be added to increase the possible accent value. For example, an accent adder field may be appended to the encrypted word. The accent adder field may contain one or more characters, preferably characters with multiple possible accent variations. As previously described, an end of word indicator field may be added to separate the encrypted word and the accent adder field. As an example referring to FIG. 7C, an exemplary combination of the encrypted word with accent 710 with an end of word indicator 712 and an accent adder 714 is shown.

As previously discussed, a base16 encoding may be used, which provides for about 10 characters available for use for special purpose. In some embodiments, one or more characters having multiple accent variations may be assigned for the special purpose of generating the accent value field. For example, character “e” may be assigned for the special purpose of generating the accent value field. In such an embodiment, the encrypted output word will not contain character “e”. So, in some embodiments, any presence of special character, for example, character “e” in the final encrypted word may indicate both end of word indicator and an accent value field.

In some other embodiments, one or more characters having multiple accent variations may be assigned for the special purpose of generating the accent adder field. For example, character “e” may be assigned for the special purpose of generating the accent adder field. In such an embodiment, the encrypted output word will not contain character “e”. So, any presence of special character, for example, character “e” in the final encrypted word may indicate both end of word indicator and an accent adder field.

In some embodiments, it may be desirable to further anonymize the accent information in the final encrypted word. For example, adding accent to the encrypted word when the input word does not have any accent. As an example, a random word may be used as an initializing vector in combination with a key to generate anonymized accent value. The anonymized accent value is then used to appropriately encode the final encrypted word to indicate the anonymized accent value, as previously described. During decoding of the final encrypted word, the anonymized accent value is decoded to determine the clear or non-anonymized accent value. Based on the non-anonymized accent value, the accent is reintroduced into the decoded encrypted output word. As one skilled in the art appreciates, an exemplary accent value of 0000 (indicative of no accent) will be modified to a different value, based on the anonymization of the accent value. The final encrypted word will be encoded with the anonymized accent value, as previously described.

Case Sensitive Words:

In some embodiments, the input word may include both upper case and lower case letters. Depending upon type of encryption used, the input word when encrypted may be converted to an encrypted word which may have more number of characters than the input word. In some embodiments, the encrypted word may be 1s and 0s that may not correlate to a character, say Unicode character. As an example, the input word “Defend” after encryption may provide encrypted data of 101011001111011111. On the other hand, an input word “defend” after encryption may provide an encrypted word of 11110000110001110. In some embodiments, there may be a need to preserve the case sensitive information. In some embodiments, case insensitive search capability may need to be preserved. Some embodiments to achieve one or more of these objectives will be described now.

As an example, based on the case information in the input word, a case information word is created. As an example, a binary value of 0 may be used for lower case character and a binary value of 1 may be used for upper case character. For the input word “Defend”, the case information word will be “100000”. Similarly, for the input word “defend”, the case information word will be “000000”.

In one embodiment, the case information word is generated for the input word. Then, the input word case is changed to all lower case. This modified input word is encrypted using an encryption strategy to generate an encrypted word. The case information word is appended to the encrypted word. Using this approach, the encrypted word for input words “Defend” and “defend” will be the same, as only the lower case letters are used. However, the appended case information word will be different. During de-anonymzation, the case information word may be used to determining the case of the characters in the input word. As previously described, a preamble may indicate the type of anonymization strategy used, for example, preserving case sensitive information.

This approach permits case insensitive search of anonymized data. In one embodiment, when a case insensitive search is performed, the search word is converted to all lower case. Then, the converted search word is used to generate the encrypted word, using the appropriate anonymization strategy. The encrypted word is sent as the search string to a destination computer and all matches of the encrypted word is retrieved. In one embodiment, the case information word may also be retrieved along with the matched encrypted word. The retrieved matched encrypted word is de-anonymized to determine the clear word. The case of each character of the retrieved clear word may then be changed based on the corresponding case information word. For example, changing the case of a character to an upper case, if the corresponding bit in the case information word is set to “1”.

In one embodiment, a suitable end of word indicator may be used between the encrypted word and the case information word so as to determine the end of the encrypted word and beginning of the case information word. As an example, letter “a” may be used as an end of word indicator. In some embodiments, the case information word will have same number of characters as the input word and there may not be a need for an end of word indicator.

As an example referring to FIG. 7D, an exemplary combination of the encrypted word 716 with case information word 718 and an end of word indicator 720 is shown. In this example, the encrypted word 716 is “11110000110001110” and the case information word 718 is “100000”, for an input word “Defend”. The end of word indicator 720 is letter “a”.

Compression:

In some embodiments, it may be desirable to anonymize certain fields, with the encrypted output word having less number of characters than the input word. As previously described, when the input word is anonymized, additional characters may be added as preamble and postamble to the encrypted output word. In some embodiments, there may be a limitation on the number of characters that may be stored in a field. Therefore, in order to store the preamble, postamble and the encrypted output word in a field with limited number of characters, there may be a need to encrypt the input word such that the number of characters in the encrypted output word is less than the number of characters in the input word. An exemplary system and method will now be described.

As an example, a field used to store state or province information may have limited number of characters, for example, 20 characters. A set of characters may be assigned to be part of a predefined common character set, for example characters A-Z and numbers 0-9. Other characters are assigned to be part of an uncommon character set. One exemplary way of encoding Unicode characters is using UTF-16, which uses two bytes for all code points up to U+FFFF and four bytes for all codepoints above that. Each byte has 8 bits. As one skilled in the art appreciates, with UTF-16, we can store up to 64K different values per character, using 16 bits.

In one embodiment, the input word is used to generate a bit stream. As an example, each character in the common character set may be represented using 6 bits of data. Each character in the uncommon character set may be represented using 16 bits. One bit may be used as a set type bit to indicate whether the character is part of the common character set or the uncommon character set. Therefore, a common character set is represented in 7 bits, a first length of the bit stream. The uncommon character set is represented in 17 bits, a second length of the bit stream. The first length of the bit stream is less than the second length of the bit stream. FIG. 7D shows an exemplary bit stream with a set type bit 722 indicative of common character set and common character set 724, with 6 bits. FIG. 7E shows an exemplart bit stream set type bit 726 indicative of uncommon character set and uncommon charater set 728 with 16 bits.

Now, a bit stream of 20 characters, with all characters part of the common character set will have 140 bits. Now, these 140 bits representing 20 characters are encoded using 16 bits per character, for example, as Unicode characters using UTF-16. 9 characters based on UTF-16 will require 144 bits, which is sufficient to encode 140 bits, leaving 4 extra bits. In one embodiment, the excess trailing bits are set to a predefined pattern, so as to identify the pattern during decoding. For example, the excess trailing bits may be set to zeros, preceded by a 1.

As an example, the last 16 bit character will have xxxxxxxxxxxx1000, where x indicates the actual binary value in the bit stream, with three zeros representing the trailing zeros, which may always be preceded by a 1, indicative of the end of the bit stream. As one skilled in the art appreciates, using this exemplary system and method, the input word of 20 characters may be encoded using 9 characters based on UTF-16. This will provide additional 11 characters available for use as preamble, postamble and other special characters as part of the final encrypted word.

In the above example, all the characters in the exemplary field are part of the common character set, thereby yielding 7 bits per character. In some embodiments, the exemplary field may contain characters that may belong to both the common character set and uncommon character set. By using appropriate set type bit, a corresponding bit stream of the characters in the field may be created. Then, the created bit stream is encoded using Unicode characters based on UTF-16. Depending upon number of characters in the field that belong to the common character set, the encrypted output word will have less number of characters than the input word.

As one skilled in the art appreciates, number of bits used to represent the common character set and the uncommon character set may be changed to represent different number of characters in the common character set and the uncommon character set, as needed,

Now referring to FIGS. 8A-8C and FIGS. 9A-9C an exemplary anonymization implementation will be described. Referring to FIG. 8A, a screen shot 800 presented on a user computer to open a new account for an application running on a hosted cloud is shown. More specifically, the data field 802 refers to the Account Name and the corresponding data for the Account Name is entered in the form field 804. For example, the Account Name entered is “Test Account 1”.

FIG. 8B shows the “POST” action data as transmitted to the hosted cloud, without anonymization. More specifically, reference numeral 804-1 shows the post data that corresponds to the Account Name. As one skilled in the art appreciates, it is not difficult to ascertain the Account Name as “Test Account 1”.

FIG. 8C shows the “POST” data as transmitted to the host cloud, with an embodiment of the anonymization technique described in this disclosure. More specifically, reference numeral 804-2 shows the anonymized data that corresponds to the Account Name. FIG. 8D shows various parts of the anonymized data.

Referring to FIG. 8D, the anonymized data 804-2 includes two anonymized data fields 804-21 and 804-22. The first data field 804-21 corresponds to the word “Test” and the second data field 804-22 corresponds to the word “Account”. Additionally, the preamble 806 for the first data field is TPF1. The postamble 808 for the first data field is 1TSF. Similarly, the preamble 810 for the second data field is TPF1 and the postamble 812 for the second data field is 1TSF1.

Now, referring to FIG. 9A, a screen shot 900 presented on a user computer to view accounts set up for an application running on a hosted cloud is shown. More specifically, the screen shot 900 shows the list of account names that have been set up, for example, account name 902 referring to Test Account 1 that is received in response to a GET action initiated by the user computer to the application hosted on the hosted cloud.

FIG. 9B shows the HTML code 904 constructed by the application hosted on the hosted cloud in response to the GET action. More specifically, the anonymized values 906 of the account name is returned in the HTML code 904. The anonymized value 906 corresponds to the anonymized data 904-2.

FIG. 9C shows the HTML code 908 constructed after processing the received HTML code 904 from the hosted cloud and de-anonymize the anomymized data field 906. More specifically, the HTML code 908 includes the de-anonymized value as shown in data field 910. The HTML code 908 with de-anonymized value for Account Name data field is sent to the user computer, in response to the GET action initiated by the user computer.

Now, referring to FIG. 10, a method of anonymization of data to be transmitted to a hosted cloud is described. In block S1002, an anonymization strategy for data anonymization is provided. In one embodiment, the anonymization strategy is stored in a anonymization strategy module 406. In one embodiment, a management console module 404 may query applications running on the hosted cloud and present and receive meta data (or data schema, data definitions) of each application. The management console module 404 may present the available anonymization strategies for each data field of the meta data to the user computer. The user may select one or more of the anonymization strategies to be applied to various data fields of the application.

The selected anonymization strategy is stored by the management console module 404 in the anonymization strategy module 406, for later use by other modules of the anonymization system 402. The anonymization strategy may be stored as a table in the anonymization strategy module 406. The anaonymization strategy in one embodiment may include schema definition for data manipulation. An exemplary selected anonymization strategy is described with reference to FIG. 5.

In block S1004 data to be transmitted to a destination is received. In one embodiment, the data to be transmitted to a destination is received by the anonymizaton system from the user computer.

In block S1006, selective anonymization of data based on anonymization strategy is performed. For example, an anonymization strategy may be selected based on the data field and corresponding anonymization strategy, as shown and described with reference to FIG. 5. An exemplary anonymization of data may be performed, as described with reference to FIGS. 8A-8D.

In some embodiments, the clear data or the input data may have accented characters, and the anonymization of the accented characters may be performed as described with reference to FIGS. 7A-7C.

In some embodiments, the clear data or input data may have case sensitive characters and anonymization of the input data may be performed as described with reference to FIG. 7D.

In some embodiments, the clear data or input data may have to be compressed when anonymized, and the anonymization of the input data with compression may be performed as described with reference to FIGS. 7E and 7F.

In block S1008, the anonymized data is transmitted to the destination. In one embodiment, the anonymized data is transmitted to the application running on the hosted cloud. An exemplary transmission of anonymozed data was at least described with reference to FIGS. 8A-8D.

In block S1010, the anonymized data is stored in the destination. For example, the anonymized data may be stored in a database coupled to an application server, as shown in FIG. 3.

Now referring to FIG. 11A, an exemplary method of processing a request at the destination is described. In block S1102, a request for the stored anonymized data is received at the destination. For example, as described with reference to FIGS. 9A-9C, a GET action request is received at the application running on a hosted cloud.

In block S1104, the request is executed at the destination to selectively retrieve stored anonymized data. As an example, the application server running on the hosted cloud processes the GET action and retrieve anonymized data corresponding to the GET action from a user computer.

In block S1106, the selectively retrieved stored anonymized data is returned. As an example, the application running on the hosted cloud assembles a response to the GET action and returns the selectively retrieved stored anonymized data, as shown in FIG. 9B.

Now referring to FIG. 11B, an exemplary method or processing a response to a request is described. In block S1108, a selectively retrieved anonymized data is response to a request is received. As an example, a response from the hosted cloud to a GET action is received by the anonymization system 402. The response may be a HTTP response as shown in FIG. 9B.

In block S1110, the retrieved stored anonymized data is de-anonymized. For example, the retrieved anonymized data is evaluated by the de-anonymization module 410. The selective preamble contained in the anonymized data is identified. The de-anonymization module performs the de-anonymization based on the identified preamble and generates the de-anonymized data. An exemplary de-anonymized data is shown in FIG. 9C.

In some embodiments, the preamble may indicate that the anonymized data includes accented characters and the de-anonymization of the anonymized data may be performed as previously described with reference to FIGS. 7A-7C and generate the accented input or clear word.

In some embodiments, the preamble may indicate that the anonymized data includes case sensitive data and de-anonymization of the anonymized data may be performed as described with reference to FIG. 7D and generate the case sensitive input or clear word.

In some embodiments, the preamble may indicate that the anonymized data is compressed and the de-anonymization of the input data with compression may be performed as described with reference to FIGS. 7E and 7F, to generate the input or clear word from the anonymized data.

In block S1112, the de-anonymized data is returned in response to the request. For example, an HTTP response as described with reference to FIG. 9C is returned to the user computer.

As it is apparent to one skilled in the art, data transfers from user applications are selectively anonymized using one or more anonymization techniques, based on selected anonymization strategy. The anonymization of data happens transparently to the user application.

Although in some embodiments, anonymization techniques may have be described with reference to encryption of the input word, these techniques may be adapted and practiced using tokenization as well.

Although in this disclosure reference is made to transmitting anonymized data to a hosted cloud or a destination system located at a third party location, the disclosure is not limited to the disclosed embodiments. In some embodiments, anonymized data may be stored in a user cloud itself. The server application may be any type of application, including word processing applications, mail applications and the like.

Although the hosted cloud has been described with one or more servers and databases, these clouds may be implemented with additional servers and databases that may act as redundant systems or cumulative systems.

As one skilled in the art appreciates, various embodiments of this disclosure may provide one or more of the following benefits. For example, anonymization of transmitted data may occur transparent to the user application. Anonymized data may be sortable, based on the selected anonymization strategy. Anonymized data may be searchable, based on the selected anonymization strategy. Anonymized data may be subjected to case-insensitive search, based on the selected anonymization strategy.

Portions of a data field may be anonymized, retaining other portion of the data field in its original form. Portions of the data field retained in its original form may be bypassed from the anonymization process and system, which may lead to improved overall performance of the anonymization system.

While embodiments of the present invention are described above with respect to what is currently considered its preferred embodiments, it is to be understood that the invention is not limited to that described above. To the contrary, the invention is intended to cover various modifications and equivalent arrangements within the spirit and scope of the appended claims. 

What is claimed is:
 1. A method for anonymizing data to be transmitted to a destination computing device, comprising: receiving data to be transmitted to the destination from a user computer, the data including a plurality of characters to form a word; determining an accent value for the word of the received data; generating an accent value word with a plurality of characters indicative of the accent value for the word of the received data; anonymizing the word of the received data using an anonymization module to derive an anonymized word of the received data; appending the accent value word to the anonymized word of the received data; and transmitting said anonymized word of the received data with the appended accent value word to the destination computing device over a network.
 2. The method of claim 1, wherein determining an accent value further including: determining whether preserving accent for the data is selected; and calculating the accent value for the word of the received data based on the selection.
 3. An anonymization system to anonymize data transmitted to a destination computing device, comprising: an anonymization strategy module to store anonymization strategy for data anonymization; a logic to receive data to be transmitted to the destination from a user computer; and an anonymization module to selectively anonymize data based on the anonymization strategy to generate an anonymyzed data, wherein, the received data includes a plurality of characters to form a word; an accent value for the word of the received data is determined; an accent value word with a plurality of characters indicative of the accent value for the word of the received data is generated; the accent value word is appended to the anonymized word of the received data; and the anonymized word of the received data with the appended accent value word is transmitted to the destination computing device over a network to the destination computing device.
 4. The system of claim 3, further including: retrieve the anonymization strategy for the data from the anonymization strategy module to determine whether preserving accent for the data is selected; and calculate the accent value for the word of the received data based on the selection.
 5. The system of claim 3, wherein the system further including: a de-anonymization module configured to receive a selectively retrieved anonymized word of the received data in response to a request; determine the accent value; de-anonymize the retrieved stored anonymized word of the received data; accent the de-anonymized word of the received data based on the accent value; return the de-anonymized word of the received data with accent, in response to the request.
 6. The system of claim 3 further including a management console module configured to receive meta data for an application executed on the destination computing device, meta data indicative of one or more data fields of the application; present at least one data field of the application along with a plurality of anonymization strategies for selection; associate one of the anonymization strategy to the at least one data field of the application, based on the selection; store the associated anonymization strategy for the at least one data field of the application, using the anonymization strategy module.
 7. The system of claim 6, wherein the Anonymization module retrieves stored anonymization strategy for one or more data fields of the data to be transmitted; performs the anonymization of original data in one or more data fields based on the retrieved anonymozation strategy for the one or more data fields; and assemble data to be transmitted, using anonymized data fields.
 8. The system of claim 7, wherein the anonymized data is configured to preserve the order of the word.
 9. The system of claim 7, wherein the anonymized data is configured to be searchable using anonymized keyword.
 10. The system of claim 3, wherein the anonymization strategy includes anonymization based on at least both of tokenization and encryption.
 11. The system of claim 3, wherein the anonymization based on tokenization includes at least one of random token anonymization, sortable token anonymization and case-insensitive searchable anonymization.
 12. The system of claim 10, wherein the anonymization based on encryption includes at least one of encryption that permits exact word matching search, encryption that permits search and encryption that permits partial string and wild card searches.
 13. A method for anonymizing data to be transmitted to a destination computing device, comprising: receiving data to be transmitted to the destination from a user computer, the data including a plurality of characters; assigning a set of characters to be part of a common character set, with characters not part of the common character set part of a uncommon character set, wherein a character part of the common character set represented by a bit stream of a first length and a character part of the uncommon character set represented by a bit stream of a second length, wherein the second length is longer than the first length; generating a bit stream for all the characters in the received data, based on each character of their assignment to the common character set and uncommon character set, with at least one character in the data belonging to the common character set; and encoding the generated bit stream as encoded characters to form an encoded data, using a bit stream length greater than the first length so as to generate encoded data with less number of characters than the received data.
 14. An anonymization system to anonymize data transmitted to a destination computing device, comprising: an anonymization strategy module to store anonymization strategy for data anonymization; a logic to receive data to be transmitted to the destination from a user computer; and an anonymization module to selectively anonymize data based on the anonymization strategy to generate an anonymized data, wherein, the received data includes a plurality of characters; a set of characters are assigned to be part of a common character set; characters not part of the common character set assigned to be part of a uncommon character set, wherein a character part of the common character set represented by a bit stream of a first length and a character part of the uncommon character set represented by a bit stream of a second length, wherein the second length is longer than the first length; a bit stream for all the characters in the received data are generated, based on each character of their assignment to the common character set and uncommon character set, with at least one character in the data belonging to the common character set; and encoding the generated bit stream as encoded characters to form an encoded data, using a bit stream length greater than the first length so as to generate encoded data with less number of characters than the received data.
 15. The system of claim 14, wherein the anonymized data is configured to preserve the order of the received data.
 16. The system of claim 14, wherein the anonymized data is configured to be searchable using anonymized keyword.
 17. The system of claim 14, wherein anonymization strategy includes at least one of tokenization and encryption, wherein anonymization based on tokenization includes at least one of random tokenization, sortable token anonymization and case-insensitive searchable anonymization; and anonymization based on encryption includes at least one of encryption that permits exact word matching search, encryption that permits search and encryption that permits partial string and wild card searches.
 18. A method for anonymizing data to be transmitted to a destination computing device, comprising: receiving data to be transmitted to the destination from a user computer, the data including a plurality of characters, with at least one character case sensitive; creating a case information word indicative of the case of the character; converting all the characters in the data to a known case; anonymizing the converted data with known case, using an anonymization module to derive an anonymized data which preserves the number of characters of the received data; appending said case information word to the anonymized data; and transmitting anonymized data and the appended case information word to the destination computing device over a network.
 19. The method of claim 18, wherein creating a case sensitive word further including: determining whether preserving case for the data is selected; and creating the case information word for the data based on the selection.
 20. An anonymization system to anonymize data transmitted to a destination computing device, comprising: an anonymization strategy module to store anonymization strategy for data anonymization; a logic to receive data to be transmitted to the destination from a user computer; and an anonymization module to selectively anonymize data based on the anonymization strategy to generate an anonymized data, wherein, the received data includes a plurality of characters, with at least one character case sensitive; a case information word for the data is created based on the case information; said case information word is appended to the anonymized data and the anonymized data and the appended case information word is transmitted to the destination computing device over a network to the destination computing device.
 21. The system of claim 20, further including: retrieve the anonymization strategy for the data from the anonymization strategy module to determine whether preserving case information for the data is selected; and create the case information word for the data based on the selection.
 22. The system of claim 20, wherein the system further including: a de-anonymization module configured to receive a selectively retrieved anonymized data along with the case information word, in response to a request; de-anonymize the retrieved stored anonymized data; modify the case of the de-anonymized data based on the case information word; return the modified de-anonymized data, in response to the request.
 23. The system of claim 20, wherein the anonymized data is configured to preserve the order of the received data.
 24. The system of claim 20, wherein the anonymized data is configured to be searchable using anonymized keyword.
 25. The system of claim 20, wherein anonymization strategy includes at least one of tokenization and encryption, wherein anonymization based on tokenization includes at least one of random tokenization, sortable token anonymization and case-insensitive searchable anonymization; and anonymization based on encryption includes at least one of encryption that permits exact word matching search, encryption that permits search and encryption that permits partial string and wild card searches. 